基于反向学习的自适应快速人工蜂群算法

doi:10.16182/j.issn1004731x.joss.201611006

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (11): 2684-2692.doi: 10.16182/j.issn1004731x.joss.201611006

基于反向学习的自适应快速人工蜂群算法

杨小健, 董毅伟

南京工业大学电子与信息工程学院,南京 211816

收稿日期:2015-02-12 修回日期:2015-05-11 出版日期:2016-11-08 发布日期:2020-08-13
作者简介:杨小健(1963-),男,江苏南京,博士, 教授,研究方向为工业控制和优化;董毅伟(1990-),男,江苏南京,硕士生,研究方向为人工智能与优化。

Adaptive Quick Artificial Bee Colony Algorithm Based on Opposition Learning

Yang Xiaojian, Dong Yiwei

College of Electronics and Information Engineering, Nanjing Tech University, Nanjing 211816, China

Received:2015-02-12 Revised:2015-05-11 Online:2016-11-08 Published:2020-08-13

摘要/Abstract

摘要： 针对人工蜂群算法收敛速度慢、收敛精度不高、易陷入局部收敛等缺点,提出一种基于反向学习的自适应快速人工蜂群算法。研究出一种新型自适应步长,使得快速人工蜂群算法跟随蜂搜索阶段的周边食物源参数自适应化,并结合反向学习策略来改进引领蜂搜索阶段。在保证收敛速度的前提下进一步提高解的全局性。在仿真实验中加入粒子群算法和布谷鸟搜索算法的函数优化实验。对标准函数的仿真结果表明,基于反向学习的自适应快速人工蜂群算法相较于标准人工蜂群算法和快速人工蜂群算法在优化性能方面得到明显改善,其优化性能也明显优于粒子群算法和布谷鸟搜索算法。

关键词: 人工蜂群算法, 自适应, 反向学习, 优化

Abstract: On the basis of analyzing such shortcomings of the artificial bee colony algorithm (ABC) as slow convergence, low convergence precision and premature convergence, the opposition-learning adaptive quick artificial bee colony algorithm (OAQABC) was proposed. A new step size was proposed, which made the around food source parameter of quick artificial bee colony algorithm (QABC) adaptive, and combined the opposition-based learning to improve the employed bee phase. The experimental results show that OAQABC has better performance than basic ABC and QABC. Also the optimization performance of OAQABC is better than particle swarm optimization (PSO) algorithm and Cuckoo Search (CS) algorithm obviously in the experiment.

Key words: artificial bee colony (ABC), adaptive, opposition-based learning, optimization

中图分类号:

TP301.6

杨小健, 董毅伟. 基于反向学习的自适应快速人工蜂群算法[J]. 系统仿真学报, 2016, 28(11): 2684-2692.

Yang Xiaojian, Dong Yiwei. Adaptive Quick Artificial Bee Colony Algorithm Based on Opposition Learning[J]. Journal of System Simulation, 2016, 28(11): 2684-2692.

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基于反向学习的自适应快速人工蜂群算法

Adaptive Quick Artificial Bee Colony Algorithm Based on Opposition Learning

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